packages <- c(
"sp.scRNAseq",
"sp.scRNAseqData",
"seqTools",
"printr",
"ggthemes",
"tidyverse"
)
purrr::walk(packages, library, character.only = TRUE)
rm(packages)
#DATA
load('../data/uObjs.rda')
st <- str_detect(colnames(countsMgfpTumor), "^s")
cObjSng <- spCounts(countsMgfpTumor[, st], countsMgfpTumorERCC[, st])
plotUnsupervisedClass(uObj_tumor, cObjSng)
plotUnsupervisedMarkers(
uObj_tumor, cObjSng,
c("Lgr5", "Muc2", "Ptprc", "Chga", "Lyz1", "Dclk1", "Slc40a1"),
pal = RColorBrewer::brewer.pal(8, "Set1")
)
#wont plot Alpi; error; why?
sn <- str_detect(colnames(countsMgfp), "^s")
commonGenes <- intersect(rownames(countsMgfp), rownames(countsRegev))
commonGenes <- intersect(commonGenes, rownames(countsMgfpTumor))
sng <- cbind(countsMgfp[commonGenes, sn], countsMgfpTumor[commonGenes, st], countsRegev[commonGenes, ])
mul <- cbind(countsMgfp[commonGenes, !sn], countsMgfpTumor[commonGenes, !st])
erccSng <- cbind(
countsMgfpERCC[, sn],
countsMgfpTumorERCC[, st],
matrix(NA, nrow = nrow(countsMgfpERCC), ncol = ncol(countsRegev))
)
erccMul <- cbind(countsMgfpERCC[, !sn], countsMgfpTumorERCC[, !st])
#setup spCounts
cObjSng <- spCounts(sng, erccSng)
## is.na(c(counts, counts.ercc) returned TRUE
cObjMul <- spCounts(mul, erccMul)
plotUnsupervisedClass(uObj_all, cObjSng)
plotUnsupervisedMarkers(
uObj_all, cObjSng,
c("Lgr5", "Muc2", "Ptprc", "Chga", "Alpi", "Lyz1", "Dclk1", "Slc40a1"),
pal = RColorBrewer::brewer.pal(8, "Set1")
)
plotUnsupervisedClass(uObj_all, cObjSng) %>%
plotData() %>%
mutate(type = if_else(Sample %in% countsMgfpTumorMeta$sample, "Tumor", "Normal")) %>%
ggplot() +
geom_point(aes(`t-SNE dim 1`, `t-SNE dim 2`, colour = type)) +
theme_few()
More blood markers
t-cell: Cd41
b-cell: Cd191 dendritic cell: Cd11c (Itgax)1
NK cell: Tbx212
Stem/precursor: Cd341
Macrophage: Cd11b (Itgam)1
Granulocyte: Arg23
Platelet: Cd91
Erythrocyte: Rhag4
#https://www.bdbiosciences.com/documents/cd_marker_handbook.pdf
#t-cell: CD3, CD4, CD8
#b-cell: CD19
#dendritic cell: CD11c (Itgax), CD123 (Il3ra)
#NK cell: Arsb, Tbx21, Ifng (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572860/)
#Stem/precursor: CD34
#Macrophage: CD11b (Itgam), Ly-71
#Granulocyte: Ceacam10, Arg2 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184787/)
#Platelet: CD41, CD9
#Erythrocyte: Rhag (https://www.bdbiosciences.com/documents/BD_Reagents_CDMarkerMouse_Poster.pdf)
#Endothelial Cell: CD106, CD31
#Epithelial Cell: CD326 (EPCAM1)
plotUnsupervisedMarkers(
uObj_all, cObjSng,
c("Cd4", "Cd19", "Tbx21", "Cd34", "Itgam", "Arg2", "Cd9", "Rhag"),
pal = RColorBrewer::brewer.pal(8, "Set1")
)
Classification from fig 1. overlayed on fig 2.
plotUnsupervisedClass(uObj_all, cObjSng) %>%
plotData() %>%
full_join(tibble(Sample = rownames(getData(uObj_tumor, "tsne")), class = getData(uObj_tumor, "classification"))) %>%
ggplot() +
geom_point(aes(`t-SNE dim 1`, `t-SNE dim 2`, colour = class)) +
theme_few()
## Joining, by = "Sample"